Benchmarking DIA data analysis workflows

Abstract

Data independent acquisition (DIA) has become a well-established method in LC-MS driven proteomics. Nonetheless, there are still a lot of possibilities at the data analysis level. By benchmarking different DIA analysis workflows using a ground-truth sample, consisting of a differential spike-in of UPS2 in a constant yeast background, we provide a roadmap for DIA data analysis of shotgun samples based on whether sensitivity, precision or accuracy is of the essence. Three different commonly used DIA software tools (DIA-NN, EncyclopeDIA and SpectronautTM) were tested in both spectral library mode and spectral library-free mode. In spectral library mode we used the independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to the classical DDA-based spectral libraries. In total we benchmarked 12 DIA workflows. DIA-NN in library-free mode or using in silico predicted libraries, together with Spectronaut in library-free mode, shows the highest sensitivity maintaining a high reproducibility and accuracy. In general, DIA-NN shows the best reproducibility, while the accuracy is comparable for all DIA workflows.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

“…In recent benchmarking studies, four datasets were generated for UPS1 spike-in to an E. coli cell digest, acquired on Orbitrap Fusion with two fixed wide-window, one inter-cycle overlapping window, and one variable wide-window DIA schemes ( 179 ). Additionally, a dataset for UPS2 spike-in to a yeast tryptic digest was acquired on a QE HF instrument with inter-cycle overlapping window scheme ( 180 ).…”

“…Very recently, Staes et al. created an UPS2 spike-in dataset to test 12 different DIA workflows together with one DDA workflow ( 180 ).…”